0.2.7

Latest

• 2x speedup in transformation runtime by removing double transformation
• joblib parallel to UnobservedComponents
• ClipOutliers transformer, Discretize Transformer, CenterLastValue - added in prep for transform template change
• bug fix on IntermittentOccurence
• minor changes to ETS, now replaces single series failure with zero fill, damped now is damped_trend
• 0.3.0 is expected to feature a breaking change to model templates in the transformation/pre-processing

0.2.6

Latest

• fix verbose > 2 error in auto_model
• use of f-strings to print some error messages. Python 3.5 may see more complicated error messages as a result.
• improved BestN (formery Best3) Ensembles, ensemble collected in dicts
• made Horizontal and BestN ensembles tolerant of a component model failure
• made Horizontal models capable of generalizing from a subset of series
• added info to model table for models that can use future_regressor
• added Datepart Regression model, sklearn regressor on time components only

0.2.5

Latest

• fix error where wide data import skipped cleaning steps
• long=True/False for all example data

0.2.4

Latest

• ARIMA to ARIMAX (for Statsmodels v0.13)
• ARIMA parallelization
• update of daily sample data, reduced space used by yearly and hourly
• n_jobs = 'auto'
• models table to extended_tutorial.md

0.2.3

Latest

• Added n_jobs parameters to pass through to joblib (although a joblib context manager is perhaps the best way)
• Added joblib multiprocessing to ETS, GLM, and FBProphet
• Fixed future warnings with pandas.DatetimeIndex.week and changes to statsmodels ETS
• standardized source code formatting

0.2.2

Latest:

• grouping/hierarchial reconciliation to GeneralTransformer
• allow wide-style data as input
• iterative imputer
• allow a list of intervals to prediction_intervals in .predict()

0.2.1

• added 'coerce_integer' to GeneralTransformer
• various minor performance improvements

0.2.0

Latest:

Added Github Pages documentation
Changed default for `series_id` so it is no longer required if univariate
Changed default of `subset` to None.
Removed `weighted` parameter, now passing weights to .fit() alone is sufficient.
Fixed a bug where 'One or more series is 90% or more NaN' was printing when it shouldn't
Fixed (or more accurately, reduced) a bug where multiple initial runs were counting as validation runs.
Fixed bug where validation subsetting was behaving oddly
Fixed bug where regressor wasn't being passed to validation.
Renamed preord_ to future_ regressor.
Renamed sample datasets.
Allowed export of result_file as .pickle along with more complete object.
Added model_interrupt parameter to allow for manually skipping models when enabled.
Made serious efforts to make the code prettier with pylint, still lots to do, however...
Improved genetic recombination so optimal models should be reached more quickly
Improved Point to Probabilistic methods:
	'historic_quantile' more stable quantile-based error ranges
	'inferred normal' Bayesian-inspired method
Metrics:
	Added Scaled Pinball Loss (SPL)
	Removed upper/lower MAE
Improved ensembling with new parameter options
	Recursive ensembling (ensemble of ensembles) now enabled
Validation:
	Added 'seasonal' validation method
Categorical transformer improved, now tolerant to leaving bounds.
Added remove_leading_zeroes option for convenience.
Added a number of new Transformer options
	Multiple new Sklearn-sourced transformers (QuantileTransformer, etc)
	SinTrend
	DifferencedDetrend
	CumSumTransformer
	PctChangeTransformer
	PositiveShift Transformer
	Log
	IntermittentOccurrence
	SeasonalDetrend
	bkfilter and cffilter
	DatepartRegression
Entirely changed the general transformer to add ~~three~~ four levels of transformation.
Allowed context_slicer to receive direct integer inputs
Added new 'Detrend' options to allow more sklearn linear models.
GLM
	Error where it apparently won't tolerate any zeroes was compensated for.
	Speed improvement.
RollingRegression
	Added SVM model
	Added option to tune some model parameters to sklearn
	Added new feature construction parameters
	Added RNNs with Keras
GluonTS:
	fixed the use of context_length, added more options to that param
Dynamic Factor added uncertainty from Statsmodels Statespace
VARMAX added uncertainty from Statsmodels Statespace
New models:
	SeasonalNaive model
	VAR from Statsmodels (faster than VARMAX statespace)
	MotifSimulation
	WindowRegression
	TensorflowSTS
	TFPRegression
	ComponentAnalysis

0.2.0a2

Alpha release of major version release 0.2.0

0.1.5

Fixed a number of package deployment issues that meant AutoTS wasn't usable for anyone except me since about version 0.0.2